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A diversified memory built-in self-repair approach for nanotechnologies

机译:用于纳米技术的多元化内存自修复方法

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Memory built in self repair (BISR) is gaining importance since several years. Because defect densities are increasing with submicron scaling, more advanced solutions may be required for memories to be produced with the upcoming nanometric CMOS process generations. This problem will be exacerbated with nanotechnologies, where defect densities are predicted to reach levels that are several orders of magnitude higher than in current CMOS technologies. For such defect densities, traditional memory repair is not adequate. This work presents a diversified repair approach merging ECC codes and self-repair, for repairing memories affected by high defect densities. The approach was validated by means of statistical fault injection simulations considering defect densities as high as 3*10/sup -2/% (3% of cells are defective). The obtained results show that the approach provides close to 100% memory yield, by means of reasonable hardware cost, for technologies of very poor quality. Thus, the extreme defect densities that many authors predict for nanotechnologies do not represent a show-stopper, at least as concerning memories.
机译:自我修复(BISR)内置的内存正在增加几年。因为缺陷密度随着亚微米缩放而增加,所以可以使用即将到来的纳米CMOS工艺世代生产的存储器需要更先进的解决方案。该问题将加剧纳米技术,其中预测缺陷密度达到比当前CMOS技术高的几个数量级的水平。对于这种缺陷密度,传统的记忆修复不足。这项工作提出了一种多样化的修复方法,合并ECC码和自修复,用于修复受高缺陷密度影响的记忆。考虑到3 * 10 / sup -2 /%(3%的细胞有缺陷),考虑到高达3 * 10 / sup -2 /%的缺陷密度,验证该方法。所获得的结果表明,该方法通过合理的硬件成本提供了接近100%的记忆产量,适用于质量差的技术。因此,许多作者预测纳米技术的极端缺陷密度不代表展示止动器,至少与记忆一样。

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